Overview

Dataset statistics

Number of variables26
Number of observations204
Missing cells58
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.6 KiB
Average record size in memory208.6 B

Variable types

Numeric13
Categorical13

Alerts

aspiration is highly overall correlated with bore and 3 other fieldsHigh correlation
body-style is highly overall correlated with height and 2 other fieldsHigh correlation
bore is highly overall correlated with aspiration and 15 other fieldsHigh correlation
city-mpg is highly overall correlated with bore and 7 other fieldsHigh correlation
compression-ratio is highly overall correlated with fuel-system and 4 other fieldsHigh correlation
curb-weight is highly overall correlated with bore and 8 other fieldsHigh correlation
drive-wheels is highly overall correlated with engine-size and 5 other fieldsHigh correlation
engine-location is highly overall correlated with bore and 3 other fieldsHigh correlation
engine-size is highly overall correlated with bore and 13 other fieldsHigh correlation
engine-type is highly overall correlated with bore and 5 other fieldsHigh correlation
fuel-system is highly overall correlated with aspiration and 10 other fieldsHigh correlation
fuel-type is highly overall correlated with bore and 4 other fieldsHigh correlation
height is highly overall correlated with body-style and 5 other fieldsHigh correlation
highway-mpg is highly overall correlated with bore and 10 other fieldsHigh correlation
horsepower is highly overall correlated with fuel-system and 4 other fieldsHigh correlation
length is highly overall correlated with bore and 9 other fieldsHigh correlation
make is highly overall correlated with bore and 8 other fieldsHigh correlation
normalized-losses is highly overall correlated with num-of-cylinders and 2 other fieldsHigh correlation
num-of-cylinders is highly overall correlated with compression-ratio and 8 other fieldsHigh correlation
num-of-doors is highly overall correlated with body-style and 3 other fieldsHigh correlation
peak-rpm is highly overall correlated with aspiration and 9 other fieldsHigh correlation
price is highly overall correlated with bore and 9 other fieldsHigh correlation
stroke is highly overall correlated with aspiration and 15 other fieldsHigh correlation
symboling is highly overall correlated with body-style and 3 other fieldsHigh correlation
wheel-base is highly overall correlated with bore and 9 other fieldsHigh correlation
width is highly overall correlated with bore and 9 other fieldsHigh correlation
fuel-type is highly imbalanced (53.7%)Imbalance
engine-location is highly imbalanced (88.9%)Imbalance
num-of-cylinders is highly imbalanced (57.5%)Imbalance
normalized-losses has 40 (19.6%) missing valuesMissing
bore has 4 (2.0%) missing valuesMissing
stroke has 4 (2.0%) missing valuesMissing
price has 4 (2.0%) missing valuesMissing
symboling has 67 (32.8%) zerosZeros

Reproduction

Analysis started2023-12-01 12:11:09.185887
Analysis finished2023-12-01 12:11:24.692804
Duration15.51 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

symboling
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82352941
Minimum-2
Maximum3
Zeros67
Zeros (%)32.8%
Negative25
Negative (%)12.3%
Memory size1.7 KiB
2023-12-01T20:11:25.007889image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2390348
Coefficient of variation (CV)1.5045422
Kurtosis-0.65623569
Mean0.82352941
Median Absolute Deviation (MAD)1
Skewness0.2147495
Sum168
Variance1.5352072
MonotonicityNot monotonic
2023-12-01T20:11:25.095109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 67
32.8%
1 54
26.5%
2 32
15.7%
3 26
 
12.7%
-1 22
 
10.8%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.8%
0 67
32.8%
1 54
26.5%
2 32
15.7%
3 26
 
12.7%
ValueCountFrequency (%)
3 26
 
12.7%
2 32
15.7%
1 54
26.5%
0 67
32.8%
-1 22
 
10.8%
-2 3
 
1.5%

normalized-losses
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51
Distinct (%)31.1%
Missing40
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean122
Minimum65
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-01T20:11:25.191417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile74
Q194
median115
Q3150
95-th percentile188
Maximum256
Range191
Interquartile range (IQR)56

Descriptive statistics

Standard deviation35.442168
Coefficient of variation (CV)0.29050957
Kurtosis0.52544039
Mean122
Median Absolute Deviation (MAD)24
Skewness0.76597642
Sum20008
Variance1256.1472
MonotonicityNot monotonic
2023-12-01T20:11:25.284458image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161 11
 
5.4%
91 8
 
3.9%
150 7
 
3.4%
134 6
 
2.9%
128 6
 
2.9%
104 6
 
2.9%
85 5
 
2.5%
94 5
 
2.5%
65 5
 
2.5%
102 5
 
2.5%
Other values (41) 100
49.0%
(Missing) 40
 
19.6%
ValueCountFrequency (%)
65 5
2.5%
74 5
2.5%
77 1
 
0.5%
78 1
 
0.5%
81 2
 
1.0%
83 3
1.5%
85 5
2.5%
87 2
 
1.0%
89 2
 
1.0%
90 1
 
0.5%
ValueCountFrequency (%)
256 1
 
0.5%
231 1
 
0.5%
197 2
 
1.0%
194 2
 
1.0%
192 2
 
1.0%
188 2
 
1.0%
186 1
 
0.5%
168 5
2.5%
164 2
 
1.0%
161 11
5.4%

make
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
toyota
32 
nissan
18 
mazda
17 
mitsubishi
13 
honda
13 
Other values (17)
111 

Length

Max length13
Median length11
Mean length6.4558824
Min length3

Characters and Unicode

Total characters1317
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowalfa-romero
2nd rowalfa-romero
3rd rowaudi
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota 32
15.7%
nissan 18
 
8.8%
mazda 17
 
8.3%
mitsubishi 13
 
6.4%
honda 13
 
6.4%
volkswagen 12
 
5.9%
subaru 12
 
5.9%
peugot 11
 
5.4%
volvo 11
 
5.4%
dodge 9
 
4.4%
Other values (12) 56
27.5%

Length

2023-12-01T20:11:25.365101image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 32
15.7%
nissan 18
 
8.8%
mazda 17
 
8.3%
mitsubishi 13
 
6.4%
honda 13
 
6.4%
volkswagen 12
 
5.9%
subaru 12
 
5.9%
peugot 11
 
5.4%
volvo 11
 
5.4%
dodge 9
 
4.4%
Other values (12) 56
27.5%

Most occurring characters

ValueCountFrequency (%)
a 152
 
11.5%
o 150
 
11.4%
s 109
 
8.3%
t 100
 
7.6%
e 80
 
6.1%
u 76
 
5.8%
n 71
 
5.4%
i 68
 
5.2%
d 63
 
4.8%
m 56
 
4.3%
Other values (15) 392
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1307
99.2%
Dash Punctuation 10
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 152
 
11.6%
o 150
 
11.5%
s 109
 
8.3%
t 100
 
7.7%
e 80
 
6.1%
u 76
 
5.8%
n 71
 
5.4%
i 68
 
5.2%
d 63
 
4.8%
m 56
 
4.3%
Other values (14) 382
29.2%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1307
99.2%
Common 10
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 152
 
11.6%
o 150
 
11.5%
s 109
 
8.3%
t 100
 
7.7%
e 80
 
6.1%
u 76
 
5.8%
n 71
 
5.4%
i 68
 
5.2%
d 63
 
4.8%
m 56
 
4.3%
Other values (14) 382
29.2%
Common
ValueCountFrequency (%)
- 10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 152
 
11.5%
o 150
 
11.4%
s 109
 
8.3%
t 100
 
7.6%
e 80
 
6.1%
u 76
 
5.8%
n 71
 
5.4%
i 68
 
5.2%
d 63
 
4.8%
m 56
 
4.3%
Other values (15) 392
29.8%

fuel-type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
gas
184 
diesel
20 

Length

Max length6
Median length3
Mean length3.2941176
Min length3

Characters and Unicode

Total characters672
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 184
90.2%
diesel 20
 
9.8%

Length

2023-12-01T20:11:25.435413image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-01T20:11:25.503984image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
gas 184
90.2%
diesel 20
 
9.8%

Most occurring characters

ValueCountFrequency (%)
s 204
30.4%
g 184
27.4%
a 184
27.4%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 672
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 204
30.4%
g 184
27.4%
a 184
27.4%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 672
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 204
30.4%
g 184
27.4%
a 184
27.4%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 204
30.4%
g 184
27.4%
a 184
27.4%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

aspiration
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
std
167 
turbo
37 

Length

Max length5
Median length3
Mean length3.3627451
Min length3

Characters and Unicode

Total characters686
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd

Common Values

ValueCountFrequency (%)
std 167
81.9%
turbo 37
 
18.1%

Length

2023-12-01T20:11:25.572738image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-01T20:11:25.645610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
std 167
81.9%
turbo 37
 
18.1%

Most occurring characters

ValueCountFrequency (%)
t 204
29.7%
s 167
24.3%
d 167
24.3%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 686
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 204
29.7%
s 167
24.3%
d 167
24.3%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 686
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 204
29.7%
s 167
24.3%
d 167
24.3%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 204
29.7%
s 167
24.3%
d 167
24.3%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

num-of-doors
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing2
Missing (%)1.0%
Memory size1.7 KiB
four
114 
two
88 

Length

Max length4
Median length4
Mean length3.5643564
Min length3

Characters and Unicode

Total characters720
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowfour
4th rowfour
5th rowtwo

Common Values

ValueCountFrequency (%)
four 114
55.9%
two 88
43.1%
(Missing) 2
 
1.0%

Length

2023-12-01T20:11:25.708576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-01T20:11:25.765657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
four 114
56.4%
two 88
43.6%

Most occurring characters

ValueCountFrequency (%)
o 202
28.1%
f 114
15.8%
u 114
15.8%
r 114
15.8%
t 88
12.2%
w 88
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 720
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 202
28.1%
f 114
15.8%
u 114
15.8%
r 114
15.8%
t 88
12.2%
w 88
12.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 720
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 202
28.1%
f 114
15.8%
u 114
15.8%
r 114
15.8%
t 88
12.2%
w 88
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 202
28.1%
f 114
15.8%
u 114
15.8%
r 114
15.8%
t 88
12.2%
w 88
12.2%

body-style
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
sedan
96 
hatchback
70 
wagon
25 
hardtop
 
8
convertible
 
5

Length

Max length11
Median length5
Mean length6.5980392
Min length5

Characters and Unicode

Total characters1346
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowhatchback
3rd rowsedan
4th rowsedan
5th rowsedan

Common Values

ValueCountFrequency (%)
sedan 96
47.1%
hatchback 70
34.3%
wagon 25
 
12.3%
hardtop 8
 
3.9%
convertible 5
 
2.5%

Length

2023-12-01T20:11:25.832832image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-01T20:11:25.896088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
sedan 96
47.1%
hatchback 70
34.3%
wagon 25
 
12.3%
hardtop 8
 
3.9%
convertible 5
 
2.5%

Most occurring characters

ValueCountFrequency (%)
a 269
20.0%
h 148
11.0%
c 145
10.8%
n 126
9.4%
e 106
 
7.9%
d 104
 
7.7%
s 96
 
7.1%
t 83
 
6.2%
b 75
 
5.6%
k 70
 
5.2%
Other values (8) 124
9.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1346
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 269
20.0%
h 148
11.0%
c 145
10.8%
n 126
9.4%
e 106
 
7.9%
d 104
 
7.7%
s 96
 
7.1%
t 83
 
6.2%
b 75
 
5.6%
k 70
 
5.2%
Other values (8) 124
9.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 1346
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 269
20.0%
h 148
11.0%
c 145
10.8%
n 126
9.4%
e 106
 
7.9%
d 104
 
7.7%
s 96
 
7.1%
t 83
 
6.2%
b 75
 
5.6%
k 70
 
5.2%
Other values (8) 124
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 269
20.0%
h 148
11.0%
c 145
10.8%
n 126
9.4%
e 106
 
7.9%
d 104
 
7.7%
s 96
 
7.1%
t 83
 
6.2%
b 75
 
5.6%
k 70
 
5.2%
Other values (8) 124
9.2%

drive-wheels
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
fwd
120 
rwd
75 
4wd
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters612
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrwd
2nd rowrwd
3rd rowfwd
4th row4wd
5th rowfwd

Common Values

ValueCountFrequency (%)
fwd 120
58.8%
rwd 75
36.8%
4wd 9
 
4.4%

Length

2023-12-01T20:11:25.965085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-01T20:11:26.022378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
fwd 120
58.8%
rwd 75
36.8%
4wd 9
 
4.4%

Most occurring characters

ValueCountFrequency (%)
w 204
33.3%
d 204
33.3%
f 120
19.6%
r 75
 
12.3%
4 9
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 603
98.5%
Decimal Number 9
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 204
33.8%
d 204
33.8%
f 120
19.9%
r 75
 
12.4%
Decimal Number
ValueCountFrequency (%)
4 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 603
98.5%
Common 9
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 204
33.8%
d 204
33.8%
f 120
19.9%
r 75
 
12.4%
Common
ValueCountFrequency (%)
4 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 612
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 204
33.3%
d 204
33.3%
f 120
19.6%
r 75
 
12.3%
4 9
 
1.5%

engine-location
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
front
201 
rear
 
3

Length

Max length5
Median length5
Mean length4.9852941
Min length4

Characters and Unicode

Total characters1017
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront

Common Values

ValueCountFrequency (%)
front 201
98.5%
rear 3
 
1.5%

Length

2023-12-01T20:11:26.086145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-01T20:11:26.146076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
front 201
98.5%
rear 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r 207
20.4%
f 201
19.8%
o 201
19.8%
n 201
19.8%
t 201
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1017
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 207
20.4%
f 201
19.8%
o 201
19.8%
n 201
19.8%
t 201
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1017
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 207
20.4%
f 201
19.8%
o 201
19.8%
n 201
19.8%
t 201
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1017
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 207
20.4%
f 201
19.8%
o 201
19.8%
n 201
19.8%
t 201
19.8%
e 3
 
0.3%
a 3
 
0.3%

wheel-base
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.806373
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-01T20:11:26.211168image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93.1
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation5.994144
Coefficient of variation (CV)0.060665561
Kurtosis1.0347339
Mean98.806373
Median Absolute Deviation (MAD)2.6
Skewness1.069138
Sum20156.5
Variance35.929762
MonotonicityNot monotonic
2023-12-01T20:11:26.291470image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.5 21
 
10.3%
93.7 20
 
9.8%
95.7 13
 
6.4%
96.5 8
 
3.9%
97.3 7
 
3.4%
98.4 7
 
3.4%
107.9 6
 
2.9%
104.3 6
 
2.9%
100.4 6
 
2.9%
96.3 6
 
2.9%
Other values (43) 104
51.0%
ValueCountFrequency (%)
86.6 2
 
1.0%
88.4 1
 
0.5%
88.6 1
 
0.5%
89.5 3
 
1.5%
91.3 2
 
1.0%
93 1
 
0.5%
93.1 5
 
2.5%
93.3 1
 
0.5%
93.7 20
9.8%
94.3 1
 
0.5%
ValueCountFrequency (%)
120.9 1
 
0.5%
115.6 2
 
1.0%
114.2 4
2.0%
113 2
 
1.0%
112 1
 
0.5%
110 3
1.5%
109.1 5
2.5%
108 1
 
0.5%
107.9 6
2.9%
106.7 1
 
0.5%

length
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.075
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-01T20:11:26.372572image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.13
Q1166.3
median173.2
Q3183.2
95-th percentile196.52
Maximum208.1
Range67
Interquartile range (IQR)16.9

Descriptive statistics

Standard deviation12.362123
Coefficient of variation (CV)0.071016073
Kurtosis-0.093408515
Mean174.075
Median Absolute Deviation (MAD)6.95
Skewness0.14986148
Sum35511.3
Variance152.82208
MonotonicityNot monotonic
2023-12-01T20:11:26.475368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.3 15
 
7.4%
188.8 11
 
5.4%
171.7 7
 
3.4%
186.7 7
 
3.4%
166.3 7
 
3.4%
186.6 6
 
2.9%
165.3 6
 
2.9%
177.8 6
 
2.9%
176.2 6
 
2.9%
175.6 5
 
2.5%
Other values (65) 128
62.7%
ValueCountFrequency (%)
141.1 1
 
0.5%
144.6 2
 
1.0%
150 3
 
1.5%
155.9 3
 
1.5%
156.9 1
 
0.5%
157.1 1
 
0.5%
157.3 15
7.4%
157.9 1
 
0.5%
158.7 3
 
1.5%
158.8 1
 
0.5%
ValueCountFrequency (%)
208.1 1
 
0.5%
202.6 2
1.0%
199.6 2
1.0%
199.2 1
 
0.5%
198.9 4
2.0%
197 1
 
0.5%
193.8 1
 
0.5%
192.7 3
1.5%
191.7 1
 
0.5%
190.9 2
1.0%

width
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.916667
Minimum60.3
Maximum72.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-01T20:11:26.556812image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.075
median65.5
Q366.9
95-th percentile70.47
Maximum72.3
Range12
Interquartile range (IQR)2.825

Descriptive statistics

Standard deviation2.1467163
Coefficient of variation (CV)0.032567124
Kurtosis0.69318046
Mean65.916667
Median Absolute Deviation (MAD)1.4
Skewness0.89704593
Sum13447
Variance4.6083908
MonotonicityNot monotonic
2023-12-01T20:11:26.652558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
63.8 24
 
11.8%
66.5 23
 
11.3%
65.4 15
 
7.4%
63.6 11
 
5.4%
68.4 10
 
4.9%
64.4 10
 
4.9%
64 9
 
4.4%
65.5 8
 
3.9%
65.2 7
 
3.4%
64.2 6
 
2.9%
Other values (34) 81
39.7%
ValueCountFrequency (%)
60.3 1
 
0.5%
61.8 1
 
0.5%
62.5 1
 
0.5%
63.4 1
 
0.5%
63.6 11
5.4%
63.8 24
11.8%
63.9 3
 
1.5%
64 9
 
4.4%
64.1 1
 
0.5%
64.2 6
 
2.9%
ValueCountFrequency (%)
72.3 1
 
0.5%
72 1
 
0.5%
71.7 3
1.5%
71.4 3
1.5%
70.9 1
 
0.5%
70.6 1
 
0.5%
70.5 1
 
0.5%
70.3 3
1.5%
69.6 2
1.0%
68.9 4
2.0%

height
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.74902
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-01T20:11:26.734442image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.775
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.4249014
Coefficient of variation (CV)0.045115268
Kurtosis-0.43966718
Mean53.74902
Median Absolute Deviation (MAD)1.6
Skewness0.076421681
Sum10964.8
Variance5.8801468
MonotonicityNot monotonic
2023-12-01T20:11:26.920894image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.8 14
 
6.9%
52 12
 
5.9%
55.7 12
 
5.9%
54.1 10
 
4.9%
54.5 10
 
4.9%
55.5 9
 
4.4%
56.7 8
 
3.9%
54.3 8
 
3.9%
52.6 7
 
3.4%
56.1 7
 
3.4%
Other values (39) 107
52.5%
ValueCountFrequency (%)
47.8 1
 
0.5%
48.8 1
 
0.5%
49.4 2
 
1.0%
49.6 4
 
2.0%
49.7 3
 
1.5%
50.2 6
2.9%
50.5 2
 
1.0%
50.6 5
 
2.5%
50.8 14
6.9%
51 1
 
0.5%
ValueCountFrequency (%)
59.8 2
 
1.0%
59.1 3
 
1.5%
58.7 4
2.0%
58.3 1
 
0.5%
57.5 3
 
1.5%
56.7 8
3.9%
56.5 2
 
1.0%
56.3 2
 
1.0%
56.2 3
 
1.5%
56.1 7
3.4%

curb-weight
Real number (ℝ)

HIGH CORRELATION 

Distinct171
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.6029
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-01T20:11:27.002154image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1900.75
Q12145
median2414
Q32939.25
95-th percentile3503.5
Maximum4066
Range2578
Interquartile range (IQR)794.25

Descriptive statistics

Standard deviation521.96082
Coefficient of variation (CV)0.20424175
Kurtosis-0.05754869
Mean2555.6029
Median Absolute Deviation (MAD)388
Skewness0.67954468
Sum521343
Variance272443.1
MonotonicityNot monotonic
2023-12-01T20:11:27.089015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2385 4
 
2.0%
1918 3
 
1.5%
2275 3
 
1.5%
1989 3
 
1.5%
2410 2
 
1.0%
2191 2
 
1.0%
2535 2
 
1.0%
2024 2
 
1.0%
2414 2
 
1.0%
4066 2
 
1.0%
Other values (161) 179
87.7%
ValueCountFrequency (%)
1488 1
0.5%
1713 1
0.5%
1819 1
0.5%
1837 1
0.5%
1874 2
1.0%
1876 2
1.0%
1889 1
0.5%
1890 1
0.5%
1900 1
0.5%
1905 1
0.5%
ValueCountFrequency (%)
4066 2
1.0%
3950 1
0.5%
3900 1
0.5%
3770 1
0.5%
3750 1
0.5%
3740 1
0.5%
3715 1
0.5%
3685 1
0.5%
3515 1
0.5%
3505 1
0.5%

engine-type
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
ohc
148 
ohcf
15 
ohcv
 
13
l
 
12
dohc
 
11
Other values (2)
 
5

Length

Max length5
Median length3
Mean length3.122549
Min length1

Characters and Unicode

Total characters637
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowdohc
2nd rowohcv
3rd rowohc
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc 148
72.5%
ohcf 15
 
7.4%
ohcv 13
 
6.4%
l 12
 
5.9%
dohc 11
 
5.4%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Length

2023-12-01T20:11:27.171186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-01T20:11:27.239770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
ohc 148
72.5%
ohcf 15
 
7.4%
ohcv 13
 
6.4%
l 12
 
5.9%
dohc 11
 
5.4%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 196
30.8%
h 188
29.5%
c 188
29.5%
f 15
 
2.4%
v 14
 
2.2%
l 12
 
1.9%
d 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 637
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 196
30.8%
h 188
29.5%
c 188
29.5%
f 15
 
2.4%
v 14
 
2.2%
l 12
 
1.9%
d 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 637
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 196
30.8%
h 188
29.5%
c 188
29.5%
f 15
 
2.4%
v 14
 
2.2%
l 12
 
1.9%
d 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 637
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 196
30.8%
h 188
29.5%
c 188
29.5%
f 15
 
2.4%
v 14
 
2.2%
l 12
 
1.9%
d 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

num-of-cylinders
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
158 
six
24 
five
 
11
eight
 
5
two
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.9019608
Min length3

Characters and Unicode

Total characters796
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowsix
3rd rowfour
4th rowfive
5th rowfive

Common Values

ValueCountFrequency (%)
four 158
77.5%
six 24
 
11.8%
five 11
 
5.4%
eight 5
 
2.5%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Length

2023-12-01T20:11:27.314781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-01T20:11:27.382278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
four 158
77.5%
six 24
 
11.8%
five 11
 
5.4%
eight 5
 
2.5%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f 169
21.2%
o 162
20.4%
r 159
20.0%
u 158
19.8%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 796
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 169
21.2%
o 162
20.4%
r 159
20.0%
u 158
19.8%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 796
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 169
21.2%
o 162
20.4%
r 159
20.0%
u 158
19.8%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 796
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 169
21.2%
o 162
20.4%
r 159
20.0%
u 158
19.8%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

engine-size
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.89216
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-01T20:11:27.458201image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197
median119.5
Q3142
95-th percentile201.65
Maximum326
Range265
Interquartile range (IQR)45

Descriptive statistics

Standard deviation41.744569
Coefficient of variation (CV)0.32897674
Kurtosis5.2690619
Mean126.89216
Median Absolute Deviation (MAD)22.5
Skewness1.9441468
Sum25886
Variance1742.609
MonotonicityNot monotonic
2023-12-01T20:11:27.538946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
122 15
 
7.4%
92 15
 
7.4%
97 14
 
6.9%
98 14
 
6.9%
108 13
 
6.4%
90 12
 
5.9%
110 12
 
5.9%
109 8
 
3.9%
120 7
 
3.4%
141 7
 
3.4%
Other values (34) 87
42.6%
ValueCountFrequency (%)
61 1
 
0.5%
70 3
 
1.5%
79 1
 
0.5%
80 1
 
0.5%
90 12
5.9%
91 5
 
2.5%
92 15
7.4%
97 14
6.9%
98 14
6.9%
103 1
 
0.5%
ValueCountFrequency (%)
326 1
 
0.5%
308 1
 
0.5%
304 1
 
0.5%
258 2
 
1.0%
234 2
 
1.0%
209 3
1.5%
203 1
 
0.5%
194 3
1.5%
183 4
2.0%
181 6
2.9%

fuel-system
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
mpfi
93 
2bbl
66 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.8970588
Min length3

Characters and Unicode

Total characters795
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi 93
45.6%
2bbl 66
32.4%
idi 20
 
9.8%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Length

2023-12-01T20:11:27.612721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-01T20:11:27.681172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
mpfi 93
45.6%
2bbl 66
32.4%
idi 20
 
9.8%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b 160
20.1%
i 144
18.1%
p 103
13.0%
f 95
11.9%
m 94
11.8%
l 80
10.1%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 715
89.9%
Decimal Number 80
 
10.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 160
22.4%
i 144
20.1%
p 103
14.4%
f 95
13.3%
m 94
13.1%
l 80
11.2%
d 29
 
4.1%
s 10
 
1.4%
Decimal Number
ValueCountFrequency (%)
2 66
82.5%
1 11
 
13.8%
4 3
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 715
89.9%
Common 80
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 160
22.4%
i 144
20.1%
p 103
14.4%
f 95
13.3%
m 94
13.1%
l 80
11.2%
d 29
 
4.1%
s 10
 
1.4%
Common
ValueCountFrequency (%)
2 66
82.5%
1 11
 
13.8%
4 3
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 795
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 160
20.1%
i 144
18.1%
p 103
13.0%
f 95
11.9%
m 94
11.8%
l 80
10.1%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

bore
Categorical

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)19.0%
Missing4
Missing (%)2.0%
Memory size1.7 KiB
3.62
23 
3.19
20 
3.15
15 
3.03
12 
2.97
12 
Other values (33)
118 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters800
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)5.0%

Sample

1st row3.47
2nd row2.68
3rd row3.19
4th row3.19
5th row3.19

Common Values

ValueCountFrequency (%)
3.62 23
 
11.3%
3.19 20
 
9.8%
3.15 15
 
7.4%
3.03 12
 
5.9%
2.97 12
 
5.9%
3.46 9
 
4.4%
3.31 8
 
3.9%
3.78 8
 
3.9%
3.43 8
 
3.9%
2.91 7
 
3.4%
Other values (28) 78
38.2%

Length

2023-12-01T20:11:27.752030image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3.62 23
 
11.5%
3.19 20
 
10.0%
3.15 15
 
7.5%
3.03 12
 
6.0%
2.97 12
 
6.0%
3.46 9
 
4.5%
3.31 8
 
4.0%
3.78 8
 
4.0%
3.43 8
 
4.0%
3.27 7
 
3.5%
Other values (28) 78
39.0%

Most occurring characters

ValueCountFrequency (%)
3 224
28.0%
. 200
25.0%
1 61
 
7.6%
2 56
 
7.0%
9 53
 
6.6%
5 43
 
5.4%
7 40
 
5.0%
6 38
 
4.8%
0 34
 
4.2%
4 33
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 600
75.0%
Other Punctuation 200
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 224
37.3%
1 61
 
10.2%
2 56
 
9.3%
9 53
 
8.8%
5 43
 
7.2%
7 40
 
6.7%
6 38
 
6.3%
0 34
 
5.7%
4 33
 
5.5%
8 18
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 224
28.0%
. 200
25.0%
1 61
 
7.6%
2 56
 
7.0%
9 53
 
6.6%
5 43
 
5.4%
7 40
 
5.0%
6 38
 
4.8%
0 34
 
4.2%
4 33
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 224
28.0%
. 200
25.0%
1 61
 
7.6%
2 56
 
7.0%
9 53
 
6.6%
5 43
 
5.4%
7 40
 
5.0%
6 38
 
4.8%
0 34
 
4.2%
4 33
 
4.1%

stroke
Categorical

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)18.0%
Missing4
Missing (%)2.0%
Memory size1.7 KiB
3.40
20 
3.15
14 
3.03
14 
3.23
14 
3.39
13 
Other values (31)
125 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters800
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)4.0%

Sample

1st row2.68
2nd row3.47
3rd row3.40
4th row3.40
5th row3.40

Common Values

ValueCountFrequency (%)
3.40 20
 
9.8%
3.15 14
 
6.9%
3.03 14
 
6.9%
3.23 14
 
6.9%
3.39 13
 
6.4%
2.64 11
 
5.4%
3.29 9
 
4.4%
3.35 9
 
4.4%
3.46 8
 
3.9%
3.58 6
 
2.9%
Other values (26) 82
40.2%

Length

2023-12-01T20:11:27.812470image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3.40 20
 
10.0%
3.23 14
 
7.0%
3.15 14
 
7.0%
3.03 14
 
7.0%
3.39 13
 
6.5%
2.64 11
 
5.5%
3.29 9
 
4.5%
3.35 9
 
4.5%
3.46 8
 
4.0%
3.50 6
 
3.0%
Other values (26) 82
41.0%

Most occurring characters

ValueCountFrequency (%)
3 226
28.2%
. 200
25.0%
4 60
 
7.5%
0 59
 
7.4%
2 59
 
7.4%
1 47
 
5.9%
5 44
 
5.5%
9 36
 
4.5%
6 32
 
4.0%
7 21
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 600
75.0%
Other Punctuation 200
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 226
37.7%
4 60
 
10.0%
0 59
 
9.8%
2 59
 
9.8%
1 47
 
7.8%
5 44
 
7.3%
9 36
 
6.0%
6 32
 
5.3%
7 21
 
3.5%
8 16
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 226
28.2%
. 200
25.0%
4 60
 
7.5%
0 59
 
7.4%
2 59
 
7.4%
1 47
 
5.9%
5 44
 
5.5%
9 36
 
4.5%
6 32
 
4.0%
7 21
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 226
28.2%
. 200
25.0%
4 60
 
7.5%
0 59
 
7.4%
2 59
 
7.4%
1 47
 
5.9%
5 44
 
5.5%
9 36
 
4.5%
6 32
 
4.0%
7 21
 
2.6%

compression-ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.148137
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-01T20:11:27.874494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.575
median9
Q39.4
95-th percentile21.84
Maximum23
Range16
Interquartile range (IQR)0.825

Descriptive statistics

Standard deviation3.9810001
Coefficient of variation (CV)0.39228876
Kurtosis5.1851875
Mean10.148137
Median Absolute Deviation (MAD)0.4
Skewness2.6020296
Sum2070.22
Variance15.848362
MonotonicityNot monotonic
2023-12-01T20:11:27.943996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 45
22.1%
9.4 26
12.7%
8.5 14
 
6.9%
9.5 13
 
6.4%
9.3 11
 
5.4%
8.7 9
 
4.4%
8 8
 
3.9%
9.2 8
 
3.9%
7 7
 
3.4%
8.6 5
 
2.5%
Other values (22) 58
28.4%
ValueCountFrequency (%)
7 7
3.4%
7.5 5
 
2.5%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
3.9%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.5%
8.5 14
6.9%
ValueCountFrequency (%)
23 5
2.5%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.5%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 3
1.5%

horsepower
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)29.2%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean104.22277
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-01T20:11:28.024525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile181.7
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.810182
Coefficient of variation (CV)0.38197202
Kurtosis2.6021936
Mean104.22277
Median Absolute Deviation (MAD)25
Skewness1.3904657
Sum21053
Variance1584.8506
MonotonicityNot monotonic
2023-12-01T20:11:28.103499image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
9.3%
70 11
 
5.4%
69 10
 
4.9%
116 9
 
4.4%
110 8
 
3.9%
95 7
 
3.4%
114 6
 
2.9%
160 6
 
2.9%
101 6
 
2.9%
62 6
 
2.9%
Other values (49) 114
55.9%
ValueCountFrequency (%)
48 1
 
0.5%
52 2
 
1.0%
55 1
 
0.5%
56 2
 
1.0%
58 1
 
0.5%
60 1
 
0.5%
62 6
 
2.9%
64 1
 
0.5%
68 19
9.3%
69 10
4.9%
ValueCountFrequency (%)
288 1
 
0.5%
262 1
 
0.5%
207 3
1.5%
200 1
 
0.5%
184 2
1.0%
182 3
1.5%
176 2
1.0%
175 1
 
0.5%
162 2
1.0%
161 2
1.0%

peak-rpm
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)11.4%
Missing2
Missing (%)1.0%
Memory size1.7 KiB
5500
37 
4800
36 
5000
26 
5200
23 
5400
13 
Other values (18)
67 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters808
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)2.5%

Sample

1st row5000
2nd row5000
3rd row5500
4th row5500
5th row5500

Common Values

ValueCountFrequency (%)
5500 37
18.1%
4800 36
17.6%
5000 26
12.7%
5200 23
11.3%
5400 13
 
6.4%
6000 9
 
4.4%
5250 7
 
3.4%
4500 7
 
3.4%
5800 7
 
3.4%
4200 5
 
2.5%
Other values (13) 32
15.7%

Length

2023-12-01T20:11:28.173400image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5500 37
18.3%
4800 36
17.8%
5000 26
12.9%
5200 23
11.4%
5400 13
 
6.4%
6000 9
 
4.5%
5250 7
 
3.5%
4500 7
 
3.5%
5800 7
 
3.5%
4200 5
 
2.5%
Other values (13) 32
15.8%

Most occurring characters

ValueCountFrequency (%)
0 414
51.2%
5 191
23.6%
4 85
 
10.5%
8 43
 
5.3%
2 38
 
4.7%
6 15
 
1.9%
1 8
 
1.0%
3 5
 
0.6%
7 5
 
0.6%
9 4
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 414
51.2%
5 191
23.6%
4 85
 
10.5%
8 43
 
5.3%
2 38
 
4.7%
6 15
 
1.9%
1 8
 
1.0%
3 5
 
0.6%
7 5
 
0.6%
9 4
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 414
51.2%
5 191
23.6%
4 85
 
10.5%
8 43
 
5.3%
2 38
 
4.7%
6 15
 
1.9%
1 8
 
1.0%
3 5
 
0.6%
7 5
 
0.6%
9 4
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 414
51.2%
5 191
23.6%
4 85
 
10.5%
8 43
 
5.3%
2 38
 
4.7%
6 15
 
1.9%
1 8
 
1.0%
3 5
 
0.6%
7 5
 
0.6%
9 4
 
0.5%

city-mpg
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.240196
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-01T20:11:28.233976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.5515126
Coefficient of variation (CV)0.25956663
Kurtosis0.56662897
Mean25.240196
Median Absolute Deviation (MAD)5
Skewness0.65591157
Sum5149
Variance42.922317
MonotonicityNot monotonic
2023-12-01T20:11:28.308251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
31 28
13.7%
19 27
13.2%
24 22
10.8%
27 14
 
6.9%
17 13
 
6.4%
26 12
 
5.9%
23 12
 
5.9%
30 8
 
3.9%
25 8
 
3.9%
28 7
 
3.4%
Other values (19) 53
26.0%
ValueCountFrequency (%)
13 1
 
0.5%
14 2
 
1.0%
15 3
 
1.5%
16 6
 
2.9%
17 13
6.4%
18 3
 
1.5%
19 27
13.2%
20 3
 
1.5%
21 7
 
3.4%
22 4
 
2.0%
ValueCountFrequency (%)
49 1
 
0.5%
47 1
 
0.5%
45 1
 
0.5%
38 7
3.4%
37 6
2.9%
36 1
 
0.5%
35 1
 
0.5%
34 1
 
0.5%
33 1
 
0.5%
32 1
 
0.5%

highway-mpg
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.769608
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-01T20:11:28.376694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334.5
95-th percentile42.85
Maximum54
Range38
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation6.8983369
Coefficient of variation (CV)0.2241932
Kurtosis0.42714988
Mean30.769608
Median Absolute Deviation (MAD)5
Skewness0.53260522
Sum6277
Variance47.587052
MonotonicityNot monotonic
2023-12-01T20:11:28.452813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 19
 
9.3%
38 17
 
8.3%
24 17
 
8.3%
30 16
 
7.8%
32 16
 
7.8%
34 14
 
6.9%
37 13
 
6.4%
28 13
 
6.4%
29 10
 
4.9%
33 9
 
4.4%
Other values (20) 60
29.4%
ValueCountFrequency (%)
16 2
 
1.0%
17 1
 
0.5%
18 2
 
1.0%
19 2
 
1.0%
20 2
 
1.0%
22 8
3.9%
23 7
 
3.4%
24 17
8.3%
25 19
9.3%
26 3
 
1.5%
ValueCountFrequency (%)
54 1
 
0.5%
53 1
 
0.5%
50 1
 
0.5%
47 2
 
1.0%
46 2
 
1.0%
43 4
 
2.0%
42 3
 
1.5%
41 3
 
1.5%
39 2
 
1.0%
38 17
8.3%

price
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct185
Distinct (%)92.5%
Missing4
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean13205.69
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-01T20:11:28.534153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6184.3
Q17775
median10270
Q316500.75
95-th percentile32603
Maximum45400
Range40282
Interquartile range (IQR)8725.75

Descriptive statistics

Standard deviation7966.9826
Coefficient of variation (CV)0.60329923
Kurtosis3.2022754
Mean13205.69
Median Absolute Deviation (MAD)3306.5
Skewness1.8058013
Sum2641138
Variance63472811
MonotonicityNot monotonic
2023-12-01T20:11:28.621889image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16500 2
 
1.0%
7775 2
 
1.0%
7295 2
 
1.0%
7957 2
 
1.0%
6229 2
 
1.0%
6692 2
 
1.0%
18150 2
 
1.0%
7609 2
 
1.0%
8921 2
 
1.0%
5572 2
 
1.0%
Other values (175) 180
88.2%
(Missing) 4
 
2.0%
ValueCountFrequency (%)
5118 1
0.5%
5151 1
0.5%
5195 1
0.5%
5348 1
0.5%
5389 1
0.5%
5399 1
0.5%
5499 1
0.5%
5572 2
1.0%
6095 1
0.5%
6189 1
0.5%
ValueCountFrequency (%)
45400 1
0.5%
41315 1
0.5%
40960 1
0.5%
37028 1
0.5%
36880 1
0.5%
36000 1
0.5%
35550 1
0.5%
35056 1
0.5%
34184 1
0.5%
34028 1
0.5%

Interactions

2023-12-01T20:11:23.274247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:10.183172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:11.195279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:12.777961image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:13.855661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:14.919189image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:15.847888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:16.825117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:17.751836image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:18.668426image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:19.649901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:21.340115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:22.358681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:23.331193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:10.274015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:11.294521image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:12.829645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:13.941099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:14.971240image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:15.902469image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:16.876664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:17.830797image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:18.722880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:19.755578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:21.394124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:22.414920image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:23.476581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:10.481752image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:11.465929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:12.958318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:14.172333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:15.105938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:16.037769image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:17.009387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:17.973903image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:18.944251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:19.922848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:21.529395image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:22.543982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:23.534745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:10.563216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:11.562079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:13.014279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:14.226578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:15.162366image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:16.091668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:17.065489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:18.028611image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:18.999721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:20.034436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:21.588232image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:22.598962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:23.594636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:10.620670image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:11.690233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:13.070478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:14.280963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:15.220215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:16.147808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:17.138517image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:18.082137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:19.055077image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:20.145479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:21.642639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:22.660185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:23.649654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:10.672679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:11.791051image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:13.123182image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:14.347307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:15.272163image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:16.200890image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:17.213960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:18.136420image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:19.108700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:20.253343image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:21.695634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:22.713649image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:23.703911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:10.723574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:11.886561image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:13.215234image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:14.423571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:15.326616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:16.256436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:17.275955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:18.191067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:19.164307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:20.396304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:21.749143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:22.770250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:23.760546image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:10.773652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:12.065639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:13.356506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:14.474129image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:15.375843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:16.307585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:17.329091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:18.240653image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:19.218496image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:20.589545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:21.802341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:22.822656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:23.814209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:10.825109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:12.194211image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:13.411096image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:14.526768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:15.443526image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:16.454558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:17.379079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:18.293608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:19.272475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:20.695817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:21.853422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:22.877629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:23.874683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:10.879055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:12.300026image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:13.487761image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:14.584846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:15.510431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:16.513893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:17.434981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:18.351928image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:19.326751image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:20.809001image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:21.912678image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:22.935301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:24.020461image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:11.022829image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:12.470120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:13.648605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:14.738242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:15.680407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:16.660491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:17.583281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:18.493855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:19.476033image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:21.005611image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:22.094978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:23.098954image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:24.077439image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:11.078078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:12.577560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:13.711047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:14.796880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:15.738441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:16.712433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:17.638543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:18.554592image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:19.533433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:21.118062image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:22.243509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:23.153542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:24.141117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:11.135742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:12.678547image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:13.783925image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:14.857666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:15.792806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:16.767503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:17.695379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:18.612320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:19.591387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:21.228456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:22.297782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-01T20:11:23.214115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-12-01T20:11:28.699793image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
aspirationbody-styleborecity-mpgcompression-ratiocurb-weightdrive-wheelsengine-locationengine-sizeengine-typefuel-systemfuel-typeheighthighway-mpghorsepowerlengthmakenormalized-lossesnum-of-cylindersnum-of-doorspeak-rpmpricestrokesymbolingwheel-basewidth
aspiration1.0000.0000.604-0.221-0.1400.3430.1210.0000.2320.1490.6100.3740.105-0.279-0.2120.2460.4090.1370.1960.0000.5160.3080.510-0.0740.2250.306
body-style0.0001.0000.2870.0030.0040.1970.2050.4510.0340.1010.1380.1710.582-0.0570.1180.3650.2710.1280.0800.7460.1950.1150.350-0.6030.4320.162
bore0.6040.2871.000-0.620-0.1650.7010.4870.9050.7260.6710.6790.7430.234-0.627-0.1160.6450.6300.1000.4560.2820.6120.6500.748-0.1890.5510.615
city-mpg-0.2210.003-0.6201.0000.479-0.8130.3790.109-0.7300.2100.3040.390-0.0760.9680.458-0.6730.357-0.0520.4240.0670.350-0.8310.429-0.011-0.503-0.692
compression-ratio-0.1400.004-0.1650.4791.000-0.2190.1180.000-0.2350.3370.5180.993-0.0000.4460.014-0.1930.4920.1030.5210.1790.614-0.1780.7830.024-0.128-0.145
curb-weight0.3430.1970.701-0.813-0.2191.0000.4560.1090.8770.3300.2910.3040.351-0.834-0.2830.8920.4920.2190.4820.2650.3890.9140.507-0.2620.7740.867
drive-wheels0.1210.2050.4870.3790.1180.4561.0000.1260.5590.4220.3860.0910.038-0.521-0.2540.4960.6010.1730.3400.0400.3610.6430.525-0.0850.4190.515
engine-location0.0000.4510.9050.1090.0000.1090.1261.0000.1860.3990.0000.000-0.119-0.107-0.024-0.0770.702NaN0.2880.0690.9460.1970.9100.191-0.200-0.055
engine-size0.2320.0340.726-0.730-0.2350.8770.5590.1861.0000.5290.3330.1550.206-0.721-0.2380.7860.5320.1440.6420.2130.5720.8280.691-0.1830.6580.774
engine-type0.1490.1010.6710.2100.3370.3300.4220.3990.5291.0000.3760.249-0.208-0.101-0.123-0.1520.624-0.0360.5470.1960.6400.0070.7210.091-0.222-0.073
fuel-system0.6100.1380.6790.3040.5180.2910.3860.0000.3330.3761.0000.9850.041-0.687-0.5180.5520.5090.1540.3740.2410.4010.6800.5790.0780.4320.569
fuel-type0.3740.1710.7430.3900.9930.3040.0910.0000.1550.2490.9851.000-0.298-0.158-0.080-0.1930.369-0.2750.1540.1480.757-0.1450.6430.208-0.276-0.232
height0.1050.5820.234-0.076-0.0000.3510.038-0.1190.206-0.2080.041-0.2981.000-0.1390.1320.5250.4630.1650.3890.5320.3380.2700.547-0.5170.6280.347
highway-mpg-0.279-0.057-0.6270.9680.446-0.834-0.521-0.107-0.721-0.101-0.687-0.158-0.1391.0000.437-0.7010.401-0.0840.5010.1300.379-0.8270.4370.060-0.548-0.705
horsepower-0.2120.118-0.1160.4580.014-0.283-0.254-0.024-0.238-0.123-0.518-0.0800.1320.4371.000-0.2070.662-0.0500.7810.3750.749-0.3890.744-0.210-0.089-0.213
length0.2460.3650.645-0.673-0.1930.8920.496-0.0770.786-0.1520.552-0.1930.525-0.701-0.2071.0000.4980.1410.3560.3600.3600.8130.468-0.3960.9140.888
make0.4090.2710.6300.3570.4920.4920.6010.7020.5320.6240.5090.3690.4630.4010.6620.4981.0000.3510.5440.2910.499-0.0130.718-0.1340.1530.105
normalized-losses0.1370.1280.100-0.0520.1030.2190.173NaN0.144-0.0360.154-0.2750.165-0.084-0.0500.1410.3511.0000.7530.6540.4850.2020.577-0.1570.2220.109
num-of-cylinders0.1960.0800.4560.4240.5210.4820.3400.2880.6420.5470.3740.1540.3890.5010.7810.3560.5440.7531.0000.1370.5120.1420.6110.098-0.111-0.041
num-of-doors0.0000.7460.2820.0670.1790.2650.0400.0690.2130.1960.2410.1480.5320.1300.3750.3600.2910.6540.1371.0000.224-0.1370.3210.676-0.457-0.212
peak-rpm0.5160.1950.6120.3500.6140.3890.3610.9460.5720.6400.4010.7570.3380.3790.7490.3600.4990.4850.5120.2241.000-0.0840.7050.289-0.321-0.202
price0.3080.1150.650-0.831-0.1780.9140.6430.1970.8280.0070.680-0.1450.270-0.827-0.3890.813-0.0130.2020.142-0.137-0.0841.0000.425-0.1490.6920.817
stroke0.5100.3500.7480.4290.7830.5070.5250.9100.6910.7210.5790.6430.5470.4370.7440.4680.7180.5770.6110.3210.7050.4251.000-0.0040.2150.236
symboling-0.074-0.603-0.189-0.0110.024-0.262-0.0850.191-0.1830.0910.0780.208-0.5170.060-0.210-0.396-0.134-0.1570.0980.6760.289-0.149-0.0041.000-0.532-0.250
wheel-base0.2250.4320.551-0.503-0.1280.7740.419-0.2000.658-0.2220.432-0.2760.628-0.548-0.0890.9140.1530.222-0.111-0.457-0.3210.6920.215-0.5321.0000.812
width0.3060.1620.615-0.692-0.1450.8670.515-0.0550.774-0.0730.569-0.2320.347-0.705-0.2130.8880.1050.109-0.041-0.212-0.2020.8170.236-0.2500.8121.000

Missing values

2023-12-01T20:11:24.250974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-01T20:11:24.522219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-01T20:11:24.639435image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
03NaNalfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212716500.0
11NaNalfa-romerogasstdtwohatchbackrwdfront94.5171.265.552.42823ohcvsix152mpfi2.683.479.01545000192616500.0
22164audigasstdfoursedanfwdfront99.8176.666.254.32337ohcfour109mpfi3.193.4010.01025500243013950.0
32164audigasstdfoursedan4wdfront99.4176.666.454.32824ohcfive136mpfi3.193.408.01155500182217450.0
42NaNaudigasstdtwosedanfwdfront99.8177.366.353.12507ohcfive136mpfi3.193.408.51105500192515250.0
51158audigasstdfoursedanfwdfront105.8192.771.455.72844ohcfive136mpfi3.193.408.51105500192517710.0
61NaNaudigasstdfourwagonfwdfront105.8192.771.455.72954ohcfive136mpfi3.193.408.51105500192518920.0
71158audigasturbofoursedanfwdfront105.8192.771.455.93086ohcfive131mpfi3.133.408.31405500172023875.0
80NaNaudigasturbotwohatchback4wdfront99.5178.267.952.03053ohcfive131mpfi3.133.407.016055001622NaN
92192bmwgasstdtwosedanrwdfront101.2176.864.854.32395ohcfour108mpfi3.502.808.81015800232916430.0
symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
194-174volvogasstdfourwagonrwdfront104.3188.867.257.53034ohcfour141mpfi3.783.159.51145400232813415.0
195-2103volvogasstdfoursedanrwdfront104.3188.867.256.22935ohcfour141mpfi3.783.159.51145400242815985.0
196-174volvogasstdfourwagonrwdfront104.3188.867.257.53042ohcfour141mpfi3.783.159.51145400242816515.0
197-2103volvogasturbofoursedanrwdfront104.3188.867.256.23045ohcfour130mpfi3.623.157.51625100172218420.0
198-174volvogasturbofourwagonrwdfront104.3188.867.257.53157ohcfour130mpfi3.623.157.51625100172218950.0
199-195volvogasstdfoursedanrwdfront109.1188.868.955.52952ohcfour141mpfi3.783.159.51145400232816845.0
200-195volvogasturbofoursedanrwdfront109.1188.868.855.53049ohcfour141mpfi3.783.158.71605300192519045.0
201-195volvogasstdfoursedanrwdfront109.1188.868.955.53012ohcvsix173mpfi3.582.878.81345500182321485.0
202-195volvodieselturbofoursedanrwdfront109.1188.868.955.53217ohcsix145idi3.013.4023.01064800262722470.0
203-195volvogasturbofoursedanrwdfront109.1188.868.955.53062ohcfour141mpfi3.783.159.51145400192522625.0